






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
   from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.config_class(**self.inputs_dict   )
       self.parent.assertTrue(hasattr(UpperCAmelCase_       ,			"tf_padding"   )   )
       self.parent.assertTrue(hasattr(UpperCAmelCase_       ,			"depth_multiplier"   )   )





class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						str       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						List[str]=13       ,			UpperCAmelCase_							:						Any=3       ,			UpperCAmelCase_							:						Union[str, Any]=32       ,			UpperCAmelCase_							:						int=0.25       ,			UpperCAmelCase_							:						Union[str, Any]=8       ,			UpperCAmelCase_							:						Dict=8       ,			UpperCAmelCase_							:						Optional[int]=6       ,			UpperCAmelCase_							:						Optional[Any]=32       ,			UpperCAmelCase_							:						Optional[int]=True       ,			UpperCAmelCase_							:						str=True       ,			UpperCAmelCase_							:						Tuple=True       ,			UpperCAmelCase_							:						str="relu6"       ,			UpperCAmelCase_							:						List[str]=1280       ,			UpperCAmelCase_							:						Optional[int]=0.1       ,			UpperCAmelCase_							:						Any=0.02       ,			UpperCAmelCase_							:						Union[str, Any]=True       ,			UpperCAmelCase_							:						Tuple=True       ,			UpperCAmelCase_							:						Any=10       ,			UpperCAmelCase_							:						Optional[Any]=None       ,			):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     parent
       SCREAMING_SNAKE_CASE							:   int						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Dict						    =     num_channels
       SCREAMING_SNAKE_CASE							:   Dict						    =     image_size
       SCREAMING_SNAKE_CASE							:   int						    =     depth_multiplier
       SCREAMING_SNAKE_CASE							:   str						    =     depth_divisible_by
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     min_depth
       SCREAMING_SNAKE_CASE							:   int						    =     expand_ratio
       SCREAMING_SNAKE_CASE							:   Tuple						    =     tf_padding
       SCREAMING_SNAKE_CASE							:   List[str]						    =     output_stride
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     first_layer_is_expansion
       SCREAMING_SNAKE_CASE							:   Any						    =     finegrained_output
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hidden_act
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier   )
       SCREAMING_SNAKE_CASE							:   Any						    =     classifier_dropout_prob
       SCREAMING_SNAKE_CASE							:   Dict						    =     use_labels
       SCREAMING_SNAKE_CASE							:   int						    =     is_training
       SCREAMING_SNAKE_CASE							:   Dict						    =     num_labels
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     initializer_range
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     scope


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     None
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     None
       if self.use_labels:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     ids_tensor([self.batch_size]       ,			self.num_labels   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     ids_tensor([self.batch_size, self.image_size, self.image_size]       ,			self.num_labels   )

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.get_config()

       return config, pixel_values, labels, pixel_labels


     def 					_A							(					self							:						Optional[int]   ):
       return MobileNetVaConfig(
           num_channels=self.num_channels       ,			image_size=self.image_size       ,			depth_multiplier=self.depth_multiplier       ,			depth_divisible_by=self.depth_divisible_by       ,			min_depth=self.min_depth       ,			expand_ratio=self.expand_ratio       ,			output_stride=self.output_stride       ,			first_layer_is_expansion=self.first_layer_is_expansion       ,			finegrained_output=self.finegrained_output       ,			hidden_act=self.hidden_act       ,			tf_padding=self.tf_padding       ,			classifier_dropout_prob=self.classifier_dropout_prob       ,			initializer_range=self.initializer_range       ,			)


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   int						    =     MobileNetVaModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,			(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )       ,			)
       self.parent.assertEqual(
           result.pooler_output.shape       ,			(self.batch_size, self.last_hidden_size)       ,			)


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.num_labels
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     MobileNetVaForImageClassification(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_       ,			labels=UpperCAmelCase_   )
       self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)   )


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.num_labels
       SCREAMING_SNAKE_CASE							:   Dict						    =     MobileNetVaForSemanticSegmentation(UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model(UpperCAmelCase_   )
       self.parent.assertEqual(
           result.logits.shape       ,			(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )       ,			)
       SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_       ,			labels=UpperCAmelCase_   )
       self.parent.assertEqual(
           result.logits.shape       ,			(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )       ,			)






     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Any								=						(
         (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     UpperCamelCase_				:		List[Any]								=						(
         {
             '''feature-extraction''': MobileNetVaModel,
             '''image-classification''': MobileNetVaForImageClassification,
             '''image-segmentation''': MobileNetVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     UpperCamelCase_				:		Any								=						False
     UpperCamelCase_				:		List[str]								=						False
     UpperCamelCase_				:		int								=						False
     UpperCamelCase_				:		str								=						False


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     MobileNetVaModelTester(self   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     MobileNetVaConfigTester(self       ,			config_class=UpperCAmelCase_       ,			has_text_modality=UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[Any]   ):
       self.config_tester.run_common_tests()


     @unittest.skip(reason="MobileNetV2 does not use inputs_embeds"   )
     def 					_A							(					self							:						List[Any]   ):
       pass


     @unittest.skip(reason="MobileNetV2 does not support input and output embeddings"   )
     def 					_A							(					self							:						Dict   ):
       pass


     @unittest.skip(reason="MobileNetV2 does not output attentions"   )
     def 					_A							(					self							:						Union[str, Any]   ):
       pass


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     model_class(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE							:   Dict						    =     [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       def check_hidden_states_output(UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Union[str, Any]   ):
         SCREAMING_SNAKE_CASE							:   Any						    =     model_class(UpperCAmelCase_   )
         model.to(UpperCAmelCase_   )
         model.eval()

         with torch.no_grad():
           SCREAMING_SNAKE_CASE							:   str						    =     model(**self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )   )

         SCREAMING_SNAKE_CASE							:   List[Any]						    =     outputs.hidden_states

         SCREAMING_SNAKE_CASE							:   Any						    =     16
         self.assertEqual(len(UpperCAmelCase_   )       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   str						    =     True
         check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     True

         check_hidden_states_output(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_   )






     @slow
     def 					_A							(					self							:						Optional[Any]   ):
       for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE							:   int						    =     MobileNetVaModel.from_pretrained(UpperCAmelCase_   )
         self.assertIsNotNone(UpperCAmelCase_   )





def 	lowerCamelCase__      (  ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   int						    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     @cached_property
     def 					_A							(					self							:						Optional[int]   ):
       return (
           MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"   ) if is_vision_available() else None
       )


     @slow
     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   int						    =     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"   ).to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.default_image_processor
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     prepare_img()
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     image_processor(images=UpperCAmelCase_       ,			return_tensors="pt"   ).to(UpperCAmelCase_   )

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   str						    =     model(**UpperCAmelCase_   )

       # verify the logits
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.Size((1, 1001)   )
       self.assertEqual(outputs.logits.shape       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.tensor([0.2_445, -1.1_993, 0.1_905]   ).to(UpperCAmelCase_   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,			UpperCAmelCase_       ,			atol=1E-4   )   )






     @slow
     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"   )
       SCREAMING_SNAKE_CASE							:   int						    =     model.to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     prepare_img()
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     image_processor(images=UpperCAmelCase_       ,			return_tensors="pt"   ).to(UpperCAmelCase_   )

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE							:   Dict						    =     torch.Size((1, 21, 65, 65)   )
       self.assertEqual(logits.shape       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   str						    =     torch.tensor(
           [
               [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
               [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
               [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
           ]       ,			device=UpperCAmelCase_       ,			)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,			UpperCAmelCase_       ,			atol=1E-4   )   )









from math import factorial







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					real
														if isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					str      		=					[1] * rank
														else:
																					A      :					Dict      		=					rank




							def __repr__(					self						)		->       List[str]:
														return (
														    f'{self.real}+'
														    f'{"+".join(str(__UpperCAmelCase						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
														)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					List[Any]      		=					self.duals.copy()
														while cur[-1] == 0:
																					cur.pop(-1						)
														return Dual(self.real    ,						__UpperCAmelCase						)




							def __add__(					self    ,						__UpperCAmelCase						)		->       Any:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					return Dual(self.real + other    ,						self.duals						)
														A      :					int      		=					self.duals.copy()
														A      :					Any      		=					other.duals.copy()
														if len(__UpperCAmelCase						) > len(__UpperCAmelCase						):
																					o_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														elif len(__UpperCAmelCase						) < len(__UpperCAmelCase						):
																					s_dual.extend([1] * (len(__UpperCAmelCase						) - len(__UpperCAmelCase						))						)
														A      :					List[str]      		=					[]
														for i in range(len(__UpperCAmelCase						)						):
																					new_duals.append(s_dual[i] + o_dual[i]						)
														return Dual(self.real + other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__add__




							def __sub__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														return self + other * -1




							def __mul__(					self    ,						__UpperCAmelCase						)		->       List[str]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Union[str, Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i * other						)
																					return Dual(self.real * other    ,						__UpperCAmelCase						)
														A      :					Optional[Any]      		=					[0] * (len(self.duals						) + len(other.duals						) + 1)
														for i, item in enumerate(self.duals						):
																					for j, jtem in enumerate(other.duals						):
																												new_duals[i + j + 1] += item * jtem
														for k in range(len(self.duals						)						):
																					new_duals[k] += self.duals[k] * other.real
														for index in range(len(other.duals						)						):
																					new_duals[index] += other.duals[index] * self.real
														return Dual(self.real * other.real    ,						__UpperCAmelCase						)

							UpperCAmelCase_						:		int								=	__mul__




							def __truediv__(					self    ,						__UpperCAmelCase						)		->       Optional[int]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					Optional[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i / other						)
																					return Dual(self.real / other    ,						__UpperCAmelCase						)
														raise ValueError




							def __floordiv__(					self    ,						__UpperCAmelCase						)		->       Union[str, Any]:
														if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[Any]      		=					[]
																					for i in self.duals:
																												new_duals.append(i // other						)
																					return Dual(self.real // other    ,						__UpperCAmelCase						)
														raise ValueError






							def __pow__(					self    ,						__UpperCAmelCase						)		->       Any:
														if n < 0 or isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																					raise ValueError('''power must be a positive integer'''						)
														if n == 0:
																					return 1
														if n == 1:
																					return self
														A      :					Dict      		=					self
														for _ in range(n - 1						):
																					x *= self
														return x






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							if not callable(lowerCamelCase_		):
														raise ValueError('''differentiate() requires a function as input for func'''		)
							if not isinstance(lowerCamelCase_     ,				(float, int)		):
														raise ValueError('''differentiate() requires a float as input for position'''		)
							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														raise ValueError('''differentiate() requires an int as input for order'''		)
							A      :					Any      		=					Dual(lowerCamelCase_     ,				1		)
							A      :					Dict      		=					func(lowerCamelCase_		)
							if order == 0:
														return result.real
							return result.duals[order - 1] * factorial(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






		def        snake_case__		(    lowerCamelCase_		):
									return y**2 * y**4

		print(differentiate(f, 9, 2))






